import matplotlib

matplotlib.use("PS")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

np.random.seed(2)
import itertools

from keras.callbacks import ReduceLROnPlateau
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical  # convert to one-hot-encoding
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split

sns.set(style="white", context="notebook", palette="deep")
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
Y_train = train["label"]
X_train = train.drop(labels=["label"], axis=1)
del train
g = sns.countplot(Y_train)
Y_train.value_counts()
X_train.isnull().any().describe()
test.isnull().any().describe()
X_train = X_train / 255.0
test = test / 255.0
X_train = X_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
Y_train = to_categorical(Y_train, num_classes=10)
random_seed = 2
X_train, X_val, Y_train, Y_val = train_test_split(
    X_train, Y_train, test_size=0.1, random_state=random_seed
)
g = plt.imshow(X_train[0][:, :, 0])
model = Sequential()
model.add(
    Conv2D(
        filters=32,
        kernel_size=(5, 5),
        padding="Same",
        activation="relu",
        input_shape=(28, 28, 1),
    )
)
model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="Same", activation="relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="Same", activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="Same", activation="relu"))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="softmax"))
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(
    optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"]
)
learning_rate_reduction = ReduceLROnPlateau(
    monitor="val_acc", patience=3, verbose=1, factor=0.5, min_lr=0.00001
)
epochs = 1  # Turn epochs to 30 to get 0.9967 accuracy
batch_size = 86
datagen = ImageDataGenerator(
    featurewise_center=False,  # set input mean to 0 over the dataset
    samplewise_center=False,  # set each sample mean to 0
    featurewise_std_normalization=False,  # divide inputs by std of the dataset
    samplewise_std_normalization=False,  # divide each input by its std
    zca_whitening=False,  # apply ZCA whitening
    rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
    zoom_range=0.1,  # Randomly zoom image
    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
    horizontal_flip=False,  # randomly flip images
    vertical_flip=False,
)  # randomly flip images
datagen.fit(X_train)
history = model.fit_generator(
    datagen.flow(X_train, Y_train, batch_size=batch_size),
    epochs=epochs,
    validation_data=(X_val, Y_val),
    verbose=2,
    steps_per_epoch=X_train.shape[0] // batch_size,
    callbacks=[learning_rate_reduction],
)
fig, ax = plt.subplots(2, 1)
ax[0].plot(history.history["loss"], color="b", label="Training loss")
ax[0].plot(history.history["val_loss"], color="r", label="validation loss", axes=ax[0])
legend = ax[0].legend(loc="best", shadow=True)
ax[1].plot(history.history["acc"], color="b", label="Training accuracy")
ax[1].plot(history.history["val_acc"], color="r", label="Validation accuracy")
legend = ax[1].legend(loc="best", shadow=True)


def plot_confusion_matrix(
    cm, classes, normalize=False, title="Confusion matrix", cmap=plt.cm.Blues
):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation="nearest", cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    if normalize:
        cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
        thresh = cm.max() / 2.0
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(
            j,
            i,
            cm[i, j],
            horizontalalignment="center",
            color="white" if cm[i, j] > thresh else "black",
        )
    plt.tight_layout()
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


Y_pred = model.predict(X_val)
Y_pred_classes = np.argmax(Y_pred, axis=1)
Y_true = np.argmax(Y_val, axis=1)
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
plot_confusion_matrix(confusion_mtx, classes=range(10))
errors = Y_pred_classes - Y_true != 0
Y_pred_classes_errors = Y_pred_classes[errors]
Y_pred_errors = Y_pred[errors]
Y_true_errors = Y_true[errors]
X_val_errors = X_val[errors]


def display_errors(errors_index, img_errors, pred_errors, obs_errors):
    """This function shows 6 images with their predicted and real labels"""
    n = 0
    nrows = 2
    ncols = 3
    fig, ax = plt.subplots(nrows, ncols, sharex=True, sharey=True)
    for row in range(nrows):
        for col in range(ncols):
            error = errors_index[n]
            ax[row, col].imshow((img_errors[error]).reshape((28, 28)))
            ax[row, col].set_title(
                "Predicted label :{}\nTrue label :{}".format(
                    pred_errors[error], obs_errors[error]
                )
            )
            n += 1


Y_pred_errors_prob = np.max(Y_pred_errors, axis=1)
true_prob_errors = np.diagonal(np.take(Y_pred_errors, Y_true_errors, axis=1))
delta_pred_true_errors = Y_pred_errors_prob - true_prob_errors
sorted_dela_errors = np.argsort(delta_pred_true_errors)
most_important_errors = sorted_dela_errors[-6:]
display_errors(
    most_important_errors, X_val_errors, Y_pred_classes_errors, Y_true_errors
)
results = model.predict(test)
results = np.argmax(results, axis=1)
results = pd.Series(results, name="Label")
submission = pd.concat([pd.Series(range(1, 28001), name="ImageId"), results], axis=1)
submission.to_csv("cnn_mnist_datagen.csv", index=False)
